class_docstr: Identical to byteformer.ByteFormer, but registered as an audio classification
  model.
docstr: Perform a forward pass on input bytes. The input is a dictionary
  containing the input tensor. The tensor is stored as an integer tensor
  of shape [batch_size, sequence_length]. Integer tensors are used because
  the tensor usually contains mask tokens.
  
  Args:
      x: A dictionary containing {"audio": audio_bytes}.
  
  Returns:
      The output logits.
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code: def forward(self, x: Dict[str, Tensor], *args, **kwargs) -> Tensor:
      """
      Perform a forward pass on input bytes. The input is a dictionary
      containing the input tensor. The tensor is stored as an integer tensor
      of shape [batch_size, sequence_length]. Integer tensors are used because
      the tensor usually contains mask tokens.
  
      Args:
          x: A dictionary containing {"audio": audio_bytes}.
  
      Returns:
          The output logits.
      """
      return super().forward(x["audio"], *args, **kwargs)
  
file_path: ml-cvnets/cvnets/models/audio_classification/audio_byteformer.py
imports: import argparse
  from typing import Dict, Union
  from torch import Tensor
  from cvnets.models import MODEL_REGISTRY
  from cvnets.models.audio_classification.base_audio_classification import BaseAudioClassification
  from cvnets.models.classification.byteformer import ByteFormer
label_desc: Use image node_id 1 for calling the AudioByteFormer obj's underlying member method code with example usage: obj.forward(x) and returns: super